The Anorexia Nervosa Genetics Initiative (ANGI): overview and methods

This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ which permits use, distribution and reproduction in any medium, provided the original work is properly cited. This author accepted manuscript is made available following 12 month embargo from date of publication (October 2018) in accordance with the publisher’s archiving policyBackground Genetic factors contribute to anorexia nervosa (AN); and the first genome-wide significant locus has been identified. We describe methods and procedures for the Anorexia Nervosa Genetics Initiative (ANGI), an international collaboration designed to rapidly recruit 13,000 individuals with AN and ancestrally matched controls. We present sample characteristics and the utility of an online eating disorder diagnostic questionnaire suitable for large-scale genetic and population research. Methods ANGI recruited from the United States (US), Australia/New Zealand (ANZ), Sweden (SE), and Denmark (DK). Recruitment was via national registers (SE, DK); treatment centers (US, ANZ, SE, DK); and social and traditional media (US, ANZ, SE). All cases had a lifetime AN diagnosis based on DSM-IV or ICD-10 criteria (excluding amenorrhea). Recruited controls had no lifetime history of disordered eating behaviors. To assess the positive and negative predictive validity of the online eating disorder questionnaire (ED100K-v1), 109 women also completed the Structured Clinical Interview for DSM-IV (SCID), Module H. Results Blood samples and clinical information were collected from 13,363 individuals with lifetime AN and from controls. Online diagnostic phenotyping was effective and efficient; the validity of the questionnaire was acceptable. Conclusions Our multi-pronged recruitment approach was highly effective for rapid recruitment and can be used as a model for efforts by other groups. High online presence of individuals with AN rendered the Internet/social media a remarkably effective recruitment tool in some countries. ANGI has substantially augmented Psychiatric Genomics Consortium AN sample collection.Please refer to published article

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment

Galanin-like peptide (GALP) is a target for regulation by leptin in the hypothalamus of the rat

Galanin-like peptide (GALP), which was recently isolated from the porcine hypothalamus, shares sequence homology with galanin and binds with high affinity to galanin receptors. To study the distribution and regulation of GALP-expressing cells in the brain, we cloned a 120 base-pair cDNA fragment of rat GALP and produced an antisense riboprobe. In situ hybridization for GALP mRNA was then performed on tissue sections throughout the forebrain of adult ovariectomized female rats. We found GALP mRNA-containing cells in the arcuate nucleus (Arc), caudal dorsomedial nucleus, median eminence and the pituitary. Because GALP mRNA in the Arc appeared to overlap with the known distribution of leptin receptor mRNA, we tested the hypothesis that GALP expression is regulated by leptin. Using in situ hybridization, we compared the number of GALP mRNA-containing cells among groups of rats that were fed ad lib or fasted for 48 h and treated with either leptin or vehicle. Fasting reduced the number of identifiable cells containing GALP mRNA in the Arc, whereas the treatment of fasted animals with leptin produced a 4-fold increase in the number of cells expressing GALP message. The presence of GALP mRNA in the hypothalamus and pituitary and its regulation by leptin suggests that GALP may have important neuroendocrine functions, including the physiological regulation of feeding, metabolism, and reproduction

Distribution and regulation of galanin receptor 1 messenger RNA in the forebrain of wild type and galanin-transgenic mice

To learn more about molecular alterations in the brain that occur as a consequence of either the chronic excess or absence of peptide neurotransmitters, we examined the impact of genetically manipulating the neuropeptide galanin on the expression of one of its cognate receptors, galanin receptor 1. First, we examined the distribution of galanin receptor 1 messenger RNA in the mouse forebrain, and found it to be abundantly expressed in many brain regions, including in numerous hypothalamic and other forebrain regions associated with neuroendocrine function. The distribution of galanin receptor 1 messenger RNA in the mouse was similar to previous reports in the rat, with additional expression noted in the caudate putamen and in several midbrain regions. Next, using quantitative in situ hybridization, we measured cellular levels of galanin receptor 1 messenger RNA in the brains of mice that either overexpress galanin (galanin transgenic) or lack a functional galanin gene (galanin knockout). We report that relative to wild-type controls, the expression of galanin receptor 1 messenger RNA was increased in discrete areas of the brain in galanin-transgenic mice, but that depletion of galanin/noradrenergic innervation to the hypothalamus with the neurotoxin 6-hydroxydopamine did not alter levels of galanin receptor 1 messenger RNA. We also report that levels of galanin receptor 1 messenger RNA were not different between galanin-knockout and wild-type mice. These results suggest that compensatory adjustments in the expression of cognate receptors represent one mechanism by which the developing nervous system attempts to maintain homeostasis in response to overexpression of a peptidergic transmitter. However, the lack of significant changes in galanin receptor 1 messenger RNA in galanin-knockout mice suggests that developmentally programmed levels of receptor expression are maintained even in the complete absence of ligand

Distribution and regulation of galanin-like peptide (GALP) in the hypothalamus of the mouse

Galanin-like peptide (GALP) is a newly discovered molecule whose expression in the brain is confined to the arcuate nucleus and median eminence. In the rat, cellular levels of GALP mRNA are reduced by fasting and reversed by peripheral administration of leptin. The purpose of this investigation was 1) to clone and map the distribution of GALP mRNA in the brain of the mouse; 2) to compare the pattern and magnitude of GALP mRNA expression in the leptin-deficient obese (ob/ob) mouse with that of wild-type controls; and 3) to examine the effects of leptin delivered into the brain on the expression of GALP mRNA in the ob/ob mouse. We report the sequence of a mouse GALP cDNA and show that GALP mRNA is expressed in the arcuate nucleus, median eminence, infundibular stalk, and the neurohypophysis of this species. The expression of GALP mRNA in the brain was markedly reduced in the ob/ob mice, compared with wild-type animals. Intracerebroventricular infusion of leptin to ob/ob mice increased both the number of GALP mRNA-expressing neurons and their content of GALP mRNA, compared with vehicle-treated controls. These observations demonstrate that GALP mRNA is induced by leptin through a direct action on the brain

A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data

<div><p>A common goal in data-analysis is to sift through a large data-matrix and detect any significant submatrices (i.e., biclusters) that have a low numerical rank. We present a simple algorithm for tackling this biclustering problem. Our algorithm accumulates information about 2-by-2 submatrices (i.e., ‘loops’) within the data-matrix, and focuses on rows and columns of the data-matrix that participate in an abundance of low-rank loops. We demonstrate, through analysis and numerical-experiments, that this loop-counting method performs well in a variety of scenarios, outperforming simple spectral methods in many situations of interest. Another important feature of our method is that it can easily be modified to account for aspects of experimental design which commonly arise in practice. For example, our algorithm can be modified to correct for controls, categorical- and continuous-covariates, as well as sparsity within the data. We demonstrate these practical features with two examples; the first drawn from gene-expression analysis and the second drawn from a much larger genome-wide-association-study (GWAS).</p></div

Contrasting a bicluster with controls.

<p>This shows the bicluster of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.g005" target="_blank">Fig 5B</a> on top, and the rest of the controls on the bottom. The control-patients have been rearranged in order of their correlation with the co-expression pattern of the bicluster. Even though a few of the controls (i.e,. ∼ 3/166) exhibit a coexpression pattern comparable to that expressed by the bicluster, the vast majority do not.</p

Illustration of the GSE48091 gene-expression data-set used in Example-A (see main text).

<p>Each row corresponds to a patient, and each column to a ‘gene’ (i.e., gene-expression measurement): the color of each pixel codes for the intensity of a particular measurement of a particular patient (see colorbar to the bottom).<i>M</i>_<i>D</i> = 340 of these patients are cases, the other <i>M</i>_<i>X</i> = 166 are controls; we group the former into the case-matrix ‘<i>D</i>’, and the latter into the control-matrix ‘<i>X</i>’.</p